Report #58675
[gotcha] AI response quality silently degrades near context window limits with no error or warning to the user
Track cumulative token usage per conversation; surface a progressive warning as the conversation approaches the context limit; implement automatic context summarization or sliding-window management before quality degrades; never let users experience the cliff without explanation.
Journey Context:
When a conversation approaches the model's context window limit, behavior doesn't fail cleanly—it degrades. The model starts 'forgetting' earlier context, producing shorter or less relevant responses, or contradicting previously established facts. There's no HTTP error, no API warning, no user-visible signal. The user just experiences the AI suddenly becoming worse, with no explanation. This is especially insidious because the degradation is gradual—each subsequent message is slightly worse—so users don't identify the cause and instead conclude the AI is generally unreliable. Some API implementations silently truncate the earliest messages to fit the window, which means the AI loses access to critical early context \(system instructions, conversation constraints, user preferences\) without anyone knowing. The fix requires proactive token tracking: count tokens per message, sum the running total, and surface a warning banner well before the limit \(e.g., at 70% capacity\). Implement automatic summarization of older messages before you hit the hard limit. The tradeoff is that summarization adds latency and may lose nuance from early messages, but it's far better than the silent degradation cliff that permanently erodes user trust.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-20T04:58:25.039475+00:00— report_created — created